Receiver-operating characteristic curve analysis in diagnostic, prognostic and predictive biomarker research.
ABSTRACT From a clinical perspective, biomarkers may have a variety of functions, which correspond to different stages in the disease development, e.g. in the progression of cancer. Biomarkers can assist in the care of patients for screening, diagnosis, prognosis, prediction and surveillance. Fundamental for the use of biomarkers in all situations is biomarker accuracy - the ability to correctly classify one condition and/or outcome from another. Receiver-operating characteristic (ROC) curve analysis is a useful tool in assessment of biomarker accuracy. Its advantages include testing accuracy across the entire range of scores and thereby not requiring a predetermined cut-off point, in addition to easily examined visual and statistical comparisons across tests or scores, and, finally, independence from outcome prevalence. Further, ROC curve analysis is a useful tool for evaluating the accuracy of a statistical model that classifies subjects into one of two categories. Diagnostic models are different from predictive and prognostic models in that the latter incorporate time-to-event analysis, for which censored data may pose a weakness of the model, or the reference standard. However, with the appropriate use of ROC curves, investigators of biomarkers can improve their research and presentation of results. ROC curves help identify the most appropriate classification rules. ROC curves avoid confounding resulting from varying thresholds with subjective ratings. The ROC curve results should always be put in perspective, because a good classifier does not guarantee the eventual clinical outcome, in particular for time-dependant events in screening, prediction, and/or prognosis studies where particular statistical precautions and methods are needed.
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ABSTRACT: To identify a body fat percentage (%BF) threshold related to an adverse cardiometabolic profile and its surrogate BMI cut-off point.Public Health Nutrition 06/2014; · 2.48 Impact Factor
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ABSTRACT: Numerous attempts for detection of circulating tumor cells (CTC) have been made to develop reliable assays for early diagnosis of cancers. In this study, we validated the application of folate receptor α (FRα) as the tumor marker to detect CTC through tumor-specific ligand PCR (LT-PCR) and assessed its utility for diagnosis of bladder transitional cell carcinoma (TCC). Immunohistochemistry for FRα was performed on ten bladder TCC tissues. Enzyme-linked immunosorbent assay (ELISA) for FRα was performed on both urine and serum specimens from bladder TCC patients (n = 64 and n = 20, respectively) and healthy volunteers (n = 20 and n = 23, respectively). Western blot analysis and qRT-PCR were performed to confirm the expression of FRα in bladder TCC cells. CTC values in 3-mL peripheral blood were measured in 57 bladder TCC patients, 48 healthy volunteers, and 15 subjects with benign urologic pathologies by the folate receptor α ligand-targeted PCR. We found that FRα protein was overexpressed in both bladder TCC cells and tissues. The levels of FRα mRNA were also much higher in bladder cancer cell lines 5637 and SW780 than those of leukocyte. Values of FRα were higher in both serum and urine specimens of bladder TCC patients than those of control. CTC values were also higher in 3-mL peripheral blood of bladder TCC patients than those of control (median 26.5 Cu/3 mL vs 14.0 Cu/3 mL). Area under the receiver operating characteristic (ROC) curve for bladder TCC detection was 0.819, 95 % CI (0.738-0.883). At the cutoff value of 15.43 Cu/3 mL, the sensitivity and the specificity for detecting bladder cancer are 82.14 and 61.9 %, respectively. We concluded that quantitation of CTCs through FRα ligand-PCR could be a promising method for noninvasive diagnosis of bladder TCC.Tumor Biology 04/2014; · 2.84 Impact Factor
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ABSTRACT: Background : The tuberculosis (TB) case detection rate has stagnated at 60% due to disorganized case finding and insensitivity of sputum smear microscopy. Of the identified TB cases, 4% die while being treated, monitored with tools that insufficiently predict failure/mortality. Objective : To explore the TBscore, a recently proposed clinical severity measure for pulmonary TB (PTB) patients, and to refine, validate, and investigate its place in case finding. Design : The TBscore's inter-observer agreement was assessed and compared to the Karnofsky Performance Score (KPS) (paper I). The TBscore's variables underlying constructs were assessed, sorting out unrelated items, proposing a more easily assessable TBscoreII, which was validated internally and externally (paper II). Finally, TBscore and TBscoreII's place in PTB-screening was examined in paper III. Results : The inter-observer variability when grading PTB patients into severity classes was moderate for both TBscore (κ W=0.52, 95% CI 0.46-0.56) and KPS (κ W=0.49, 95% CI 0.33-0.65). KPS was influenced by HIV status, whereas TBscore was unaffected by it. In paper II, proposed TBscoreII was validated internally, in Guinea-Bissau, and externally, in Ethiopia. In both settings, a failure to bring down the score by ≥25% from baseline to 2 months of treatment predicted subsequent failure (p=0.007). Finally, in paper III, TBscore and TBscoreII were assessed in health-care-seeking adults and found to be higher in PTB-diagnosed patients, 4.9 (95% CI 4.6-5.2) and 3.9 (95% CI 3.8-4.0), respectively, versus patients not diagnosed with PTB, 3.0 (95% CI 2.7-3.2) and 2.4 (95% CI 2.3-2.5), respectively. Had we referred only patients with cough >2 weeks to sputum smear, we would have missed 32.1% of the smear confirmed cases in our cohort. A TBscoreII>=2 missed 8.6%. Conclusions : TBscore and TBscoreII are useful monitoring tools for PTB patients on treatment, as they could fill the void which currently exists in risk grading of patients. They may also have a role in PTB screening; however, this requires our findings to be repeated elsewhere.Global Health Action 05/2014; 7:24303. · 1.65 Impact Factor